Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-04-22
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-04-21"3.1 state level data
First check the 20 states with the largest number of deaths.
## date state fips cases deaths
## 2756 2020-04-21 New York 36 251720 14828
## 2754 2020-04-21 New Jersey 34 92387 4753
## 2746 2020-04-21 Michigan 26 32935 2698
## 2745 2020-04-21 Massachusetts 25 41199 1961
## 2763 2020-04-21 Pennsylvania 42 35384 1620
## 2737 2020-04-21 Illinois 17 33059 1479
## 2729 2020-04-21 Connecticut 9 20360 1423
## 2742 2020-04-21 Louisiana 22 24854 1405
## 2727 2020-04-21 California 6 35844 1316
## 2732 2020-04-21 Florida 12 27861 866
## 2733 2020-04-21 Georgia 13 19189 810
## 2774 2020-04-21 Washington 53 12345 683
## 2738 2020-04-21 Indiana 18 12097 630
## 2744 2020-04-21 Maryland 24 14193 584
## 2760 2020-04-21 Ohio 39 13725 557
## 2769 2020-04-21 Texas 48 20949 552
## 2728 2020-04-21 Colorado 8 10447 484
## 2773 2020-04-21 Virginia 51 9630 325
## 2776 2020-04-21 Wisconsin 55 4620 243
## 2749 2020-04-21 Missouri 29 5941 221For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 20 counties with the largest number of deaths.
## date county state fips cases deaths
## 77415 2020-04-21 New York City New York NA 139335 10301
## 77414 2020-04-21 Nassau New York 36059 31079 1717
## 76973 2020-04-21 Wayne Michigan 26163 14255 1278
## 76334 2020-04-21 Cook Illinois 17031 23181 1002
## 77434 2020-04-21 Suffolk New York 36103 28154 918
## 77442 2020-04-21 Westchester New York 36119 24655 904
## 77344 2020-04-21 Essex New Jersey 34013 11128 849
## 77339 2020-04-21 Bergen New Jersey 34003 13356 835
## 75950 2020-04-21 Los Angeles California 6037 15140 663
## 76043 2020-04-21 Fairfield Connecticut 9001 8472 544
## 77346 2020-04-21 Hudson New Jersey 34017 11636 525
## 76954 2020-04-21 Oakland Michigan 26125 6306 506
## 76941 2020-04-21 Macomb Michigan 26099 4544 445
## 76888 2020-04-21 Middlesex Massachusetts 25017 9621 428
## 77357 2020-04-21 Union New Jersey 34039 10289 427
## 76044 2020-04-21 Hartford Connecticut 9003 3951 402
## 77808 2020-04-21 Philadelphia Pennsylvania 42101 10028 394
## 78392 2020-04-21 King Washington 53033 5381 374
## 77349 2020-04-21 Middlesex New Jersey 34023 8767 360
## 76808 2020-04-21 Orleans Louisiana 22071 6169 344For these 20 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4